Building a Secure and Compliant Azure AI Landing Zone: Policy Framework & Best Practices
As organizations accelerate their AI adoption on Microsoft Azure, governance, compliance, and security become critical pillars for success. Deploying AI workloads without a structured compliance framework can expose enterprises to data privacy issues, misconfigurations, and regulatory risks. To address this challenge, the Azure AI Landing Zone provides a scalable and secure foundation — bringing together Azure Policy, Blueprints, and Infrastructure-as-Code (IaC) to ensure every resource aligns with organizational and regulatory standards. The Azure Policy & Compliance Framework acts as the governance backbone of this landing zone. It enforces consistency across environments by applying policy definitions, initiatives, and assignments that monitor and remediate non-compliant resources automatically. This blog will guide you through: 🧭 The architecture and layers of an AI Landing Zone 🧩 How Azure Policy as Code enables automated governance ⚙️ Steps to implement and deploy policies using IaC pipelines 📈 Visualizing compliance flows for AI-specific resources What is Azure AI Landing Zone (AI ALZ)? AI ALZ is a foundational architecture that integrates core Azure services (ML, OpenAI, Cognitive Services) with best practices in identity, networking, governance, and operations. To ensure consistency, security, and responsibility, a robust policy framework is essential. Policy & Compliance in AI ALZ Azure Policy helps enforce standards across subscriptions and resource groups. You define policies (single rules), group them into initiatives (policy sets), and assign them with certain scopes & exemptions. Compliance reporting helps surface noncompliant resources for mitigation. In AI workloads, some unique considerations: Sensitive data (PII, models) Model accountability, logging, audit trails Cost & performance from heavy compute usage Preview features and frequent updates Scope This framework covers: Azure Machine Learning (AML) Azure API Management Azure AI Foundry Azure App Service Azure Cognitive Services Azure OpenAI Azure Storage Accounts Azure Databases (SQL, Cosmos DB, MySQL, PostgreSQL) Azure Key Vault Azure Kubernetes Service Core Policy Categories 1. Networking & Access Control Restrict resource deployment to approved regions (e.g., Europe only). Enforce private link and private endpoint usage for all critical resources. Disable public network access for workspaces, storage, search, and key vaults. 2. Identity & Authentication Require user-assigned managed identities for resource access. Disable local authentication; enforce Microsoft Entra ID (Azure AD) authentication. 3. Data Protection Enforce encryption at rest with customer-managed keys (CMK). Restrict public access to storage accounts and databases. 4. Monitoring & Logging Deploy diagnostic settings to Log Analytics for all key resources. Ensure activity/resource logs are enabled and retained for at least one year. 5. Resource-Specific Guardrails Apply built-in and custom policy initiatives for OpenAI, Kubernetes, App Services, Databases, etc. A detailed list of all policies is bundled and attached at the end of this blog. Be sure to check it out for a ready-to-use Excel file—perfect for customer workshops—which includes policy type (Standalone/Initiative), origin (Built-in/Custom), and more. Implementation: Policy-as-Code using EPAC To turn policies from Excel/JSON into operational governance, Enterprise Policy as Code (EPAC) is a powerful tool. EPAC transforms policy artifacts into a desired state repository and handles deployment, lifecycle, versioning, and CI/CD automation. What is EPAC & Why Use It? EPAC is a set of PowerShell scripts / modules to deploy policy definitions, initiatives, assignments, role assignments, exemptions. Enterprise Policy As Code (EPAC) It supports CI/CD integration (GitHub Actions, Azure DevOps) so policy changes can be treated like code. It handles ordering, dependency resolution, and enforcement of a “desired state” — any policy resources not in your repo may be pruned (depending on configuration). It integrates with Azure Landing Zones (including governance baseline) out of the box. References & Further Reading EPAC GitHub Repository Advanced Azure Policy management - Microsoft Learn [Advanced A...Framework] How to deploy Azure policies the DevOps way [How to dep...- Rabobank]
Architecting an Azure AI Hub-and-Spoke Landing Zone for Multi-Tenant Enterprises
A large enterprise customer adopting AI at scale typically needs three non‑negotiables in its AI foundation: End‑to‑end tenant isolation across network, identity, compute, and data Secure, governed traffic flow from users to AI services Transparent chargeback/showback for shared AI and platform services At the same time, the platform must enable rapid onboarding of new tenants or applications and scale cleanly from proof‑of‑concept to production. This article proposes an Azure Landing Zone–aligned architecture using a Hub‑and‑Spoke model, where: The AI Hub centralizes shared services and governance AI Spokes host tenant‑dedicated AI resources Application logic and AI agents run on AKS The result is a secure, scalable, and operationally efficient enterprise AI foundation. 1. Architecture goals & design principles Goals Host application logic and AI agents on Azure Kubernetes Service (AKS) as custom deployments instead of using agents under Azure AI Foundry Enforce strong tenant isolation across all layers Support cross chargeback and cost attribution Adopt a Hub‑and‑Spoke model with clear separation of shared vs. tenant‑specific services Design principles (Azure Landing Zone aligned) Azure Landing Zone (ALZ) guidance emphasizes: Separation of platform and workload subscriptions Management groups and policy inheritance Centralized connectivity using hub‑and‑spoke networking Policy‑driven governance and automation For infrastructure as code, ALZ‑aligned deployments typically use Bicep or Terraform, increasingly leveraging Azure Verified Modules (AVM) for consistency and long‑term maintainability. 2. Subscription & management group model A practical enterprise layout looks like this: Tenant Root Management Group o Platform Management Group Connectivity subscription (Hub VNet, Firewall, DNS, ExpressRoute/VPN) Management subscription (Log Analytics, Monitor) Security subscription (Defender for Cloud, Sentinel if required) o AI Hub Management Group AI Hub subscription (shared AI and governance services) o AI Spokes Management Group One subscription per tenant, business unit, or regulated boundary This structure supports enterprise‑scale governance while allowing teams to operate independently within well‑defined guardrails. 3. Logical architecture — AI Hub vs. AI Spoke AI Hub (central/shared services) The AI Hub acts as the governed control plane for AI consumption: Ingress & edge security: Azure Application Gateway with WAF (or Front Door for global scenarios) Central egress control: Azure Firewall with forced tunneling API governance: Azure API Management (private/internal mode) Shared AI services: Azure OpenAI (shared deployments where appropriate), safety controls Monitoring & observability: Azure Monitor, Log Analytics, centralized dashboards Governance: Azure Policy, RBAC, naming and tagging standards All tenant traffic enters through the hub, ensuring consistent enforcement of security, identity, and usage policies. AI Spoke (tenant‑dedicated services) Each AI Spoke provides a tenant‑isolated data and execution plane: Tenant‑dedicated storage accounts and databases Vector stores and retrieval systems (Azure AI Search with isolated indexes or services) AKS runtime for tenant‑specific AI agents and backend services Tenant‑scoped keys, secrets, and identities 4. Logical architecture diagram (Hub vs. Spoke) 5. Network architecture — Hub and Spoke 6. Tenant onboarding & isolation strategy Tenant onboarding flow Tenant onboarding is automated using a landing zone vending model: Request new tenant or application Provision a spoke subscription and baseline policies Deploy spoke VNet and peer to hub Configure private DNS and firewall routes Deploy AKS tenancy and data services Register identities and API subscriptions Enable monitoring and cost attribution This approach enables consistent, repeatable onboarding with minimal manual effort. Isolation by design Network: Dedicated VNets, private endpoints, no public AI endpoints Identity: Microsoft Entra ID with tenant‑aware claims and conditional access Compute: AKS isolation using namespaces, node pools, or dedicated clusters Data: Per‑tenant storage, databases, and vector indexes 7. Identity & access management (Microsoft Entra ID) Key IAM practices include: Central Microsoft Entra ID tenant for authentication and authorization Application and workload identities using managed identities Tenant context enforced at API Management and propagated downstream Conditional Access and least‑privilege RBAC This ensures zero‑trust access while supporting both internal and partner scenarios. 8. Secure traffic flow (end‑to‑end) User accesses application via Application Gateway + WAF Traffic inspected and routed through Azure Firewall API Management validates identity, quotas, and tenant context AKS workloads invoke AI services over Private Link Responses return through the same governed path This pattern provides full auditability, threat protection, and policy enforcement. 9. AKS multitenancy options Model When to use Characteristics Namespace per tenant Default Cost‑efficient, logical isolation Dedicated node pools Medium isolation Reduced noisy‑neighbor risk Dedicated AKS cluster High compliance Maximum isolation, higher cost Enterprises typically adopt a tiered approach, choosing the isolation level per tenant based on regulatory and risk requirements. 10. Cost management & chargeback model Tagging strategy (mandatory) tenantId costCenter application environment owner Enforced via Azure Policy across all subscriptions. Chargeback approach Dedicated spoke resources: Direct attribution via subscription and tags Shared hub resources: Allocated using usage telemetry o API calls and token usage from API Management o CPU/memory usage from AKS namespaces Cost data is exported to Azure Cost Management and visualized using Power BI to support showback and chargeback. 11. Security controls checklist Private endpoints for AI services, storage, and search No public network access for sensitive services Azure Firewall for centralized egress and inspection WAF for OWASP protection Azure Policy for governance and compliance 12. Deployment & automation Foundation: Azure Landing Zone accelerators (Bicep or Terraform) Workloads: Modular IaC for hub and spokes AKS apps: GitOps (Flux or Argo CD) Observability: Policy‑driven diagnostics and centralized logging 13. Final thoughts This Azure AI Landing Zone design provides a repeatable, secure, and enterprise‑ready foundation for any large customer adopting AI at scale. By combining: Hub‑and‑Spoke networking AKS‑based AI agents Strong tenant isolation FinOps‑ready chargeback Azure Landing Zone best practices organizations can confidently move AI workloads from experimentation to production—without sacrificing security, governance, or cost transparency. Disclaimer: While the above article discusses hosting custom agents on AKS alongside customer-developed application logic, the following sections focus on a baseline deployment model with no customizations. This approach uses Azure AI Foundry, where models and agents are fully managed by Azure, with centrally governed LLMs(AI Hub) hosted in Azure AI Foundry and agents deployed in a spoke environment. 🚀 Get Started: Building a Secure & Scalable Azure AI Platform To help you accelerate your Azure AI journey, Microsoft and the community provide several reference architectures, solution accelerators, and best-practice guides. Together, these form a strong foundation for designing secure, governed, and cost-efficient GenAI and AI workloads at scale. Below is a recommended starting path. 1️⃣ AI Landing Zone (Foundation) Purpose: Establish a secure, enterprise-ready foundation for AI workloads. The AI Landing Zone extends the standard Azure Landing Zone with AI-specific considerations such as: Network isolation and hub-spoke design Identity and access control for AI services Secure connectivity to data sources Alignment with enterprise governance and compliance 🔗 AI Landing Zone (GitHub): https://github.com/Azure/AI-Landing-Zones?tab=readme-ov-file 👉 Start here if you want a standardized baseline before onboarding any AI workloads. 2️⃣ AI Hub Gateway – Solution Accelerator Purpose: Centralize and control access to AI services across multiple teams or customers. The AI Hub Gateway Solution Accelerator helps you: Expose AI capabilities (models, agents, APIs) via a centralized gateway Apply consistent security, routing, and traffic controls Support both Chat UI and API-based consumption Enable multi-team or multi-tenant AI usage patterns 🔗 AI Hub Gateway Solution Accelerator: https://github.com/mohamedsaif/ai-hub-gateway-landing-zone?tab=readme-ov-file 👉 Ideal when you want a shared AI platform with controlled access and visibility. 3️⃣ Citadel Governance Hub (Advanced Governance) Purpose: Enforce strong governance, compliance, and guardrails for AI usage. The Citadel Governance Hub builds on top of the AI Hub Gateway and focuses on: Policy enforcement for AI usage Centralized governance controls Secure onboarding of teams and workloads Alignment with enterprise risk and compliance requirements 🔗 Citadel Governance Hub (README): https://github.com/Azure-Samples/ai-hub-gateway-solution-accelerator/blob/citadel-v1/README.md 👉 Recommended for regulated environments or large enterprises with strict governance needs. 4️⃣ AKS Cost Analysis (Operational Excellence) Purpose: Understand and optimize the cost of running AI workloads on AKS. AI platforms often rely on AKS for agents, inference services, and gateways. This guide explains: How AKS costs are calculated How to analyze node, pod, and workload costs Techniques to optimize cluster spend 🔗 AKS Cost Analysis: https://learn.microsoft.com/en-us/azure/aks/cost-analysis 👉 Use this early to avoid unexpected cost overruns as AI usage scales. 5️⃣ AKS Multi-Tenancy & Cluster Isolation Purpose: Safely run workloads for multiple teams or customers on AKS. This guidance covers: Namespace vs cluster isolation strategies Security and blast-radius considerations When to use shared clusters vs dedicated clusters Best practices for multi-tenant AKS platforms 🔗 AKS Multi-Tenancy & Cluster Isolation: https://learn.microsoft.com/en-us/azure/aks/operator-best-practices-cluster-isolation 👉 Critical reading if your AI platform supports multiple teams, business units, or customers. 🧭 Suggested Learning Path If you’re new, follow this order: AI Landing Zone → build the foundation AI Hub Gateway → centralize AI access Citadel Governance Hub → enforce guardrails AKS Cost Analysis → control spend AKS Multi-Tenancy → scale securelyDeploy PostgreSQL on Azure VMs with Azure NetApp Files: Production-Ready Infrastructure as Code
PostgreSQL is a popular open‑source cloud database for modern web applications and AI/ML workloads, and deploying it on Azure VMs with high‑performance storage should be simple. In practice, however, using Azure NetApp Files requires many coordinated steps—from provisioning networking and storage to configuring NFS, installing and initializing PostgreSQL, and maintaining consistent, secure, and high‑performance environments across development, test, and production. To address this complexity, we’ve built production‑ready Infrastructure as Code templates that fully automate the deployment, from infrastructure setup to database initialization, ensuring PostgreSQL runs on high‑performance Azure NetApp Files storage from day one.What's New with Azure NetApp Files VS Code Extension
The latest update to the Azure NetApp Files (ANF) VS Code Extension introduces powerful enhancements designed to simplify cloud storage management for developers. From multi-tenant support to intuitive right-click mounting and AI-powered commands, this release focuses on improving productivity and streamlining workflows within Visual Studio Code. Explore the new features, learn how they accelerate development, and see why this extension is becoming an essential tool for cloud-native applications.Streamline Azure NetApp Files Management—Right from Your IDE
The Azure NetApp Files VS Code Extension is designed to streamline storage provisioning and management directly within the developer’s IDE. Traditional workflows often require extensive portal navigation, manual configuration, and policy management, leading to inefficiencies and context switching. The extension addresses these challenges by enabling AI-powered automation through natural language commands, reducing provisioning time from hours to minutes while minimizing errors and improving compliance. Key capabilities include generating production-ready ARM templates, validating resources, and delivering optimization insights—all without leaving the coding environment.How to Modernise a Microsoft Access Database (Forms + VBA) to Node.JS, OpenAPI and SQL Server
Microsoft Access has played a significant role in enterprise environments for over three decades. Released in November 1992, its flexibility and ease of use made it a popular choice for organizations of all sizes—from FTSE250 companies to startups and the public sector. The platform enables rapid development of graphical user interfaces (GUIs) paired with relational databases, allowing users to quickly create professional-looking applications. Developers, data architects, and power users have all leveraged Microsoft Access to address various enterprise challenges. Its integration with Microsoft Visual Basic for Applications (VBA), an object-based programming language, ensured that Access solutions often became central to business operations. Unsurprisingly, modernizing these applications is a common requirement in contemporary IT engagements as thse solutions lead to data fragmentation, lack of integration into master data systems, multiple copies of the same data replicated across each access database and so on. At first glance, upgrading a Microsoft Access application may seem simple, given its reliance on forms, VBA code, queries, and tables. However, substantial complexity often lurks beneath this straightforward exterior. Modernization efforts must consider whether to retain the familiar user interface to reduce staff retraining, how to accurately re-implement business logic, strategies for seamless data migration, and whether to introduce an API layer for data access. These factors can significantly increase the scope and effort required to deliver a modern equivalent, especially when dealing with numerous web forms, making manual rewrites a daunting task. This is where GitHub Copilot can have a transformative impact, dramatically reducing redevelopment time. By following a defined migration path, it is possible to deliver a modernized solution in as little as two weeks. In this blog post, I’ll walk you through each tier of the application and give you example prompts used at each stage. 🏛️Architecture Breakdown: The N-Tier Approach Breaking down the application architecture reveals a classic N-Tier structure, consisting of a presentation layer, business logic layer, data access layer, and data management layer. 💫First-Layer Migration: Migrating a Microsoft Access Database to SQL Server The migration process began with the database layer, which is typically the most straightforward to move from Access to another relational database management system (RDBMS). In this case, SQL Server was selected to leverage the SQL Server Migration Assistant (SSMA) for Microsoft Access—a free tool from Microsoft that streamlines database migration to SQL Server, Azure SQL Database, or Azure SQL Database Managed Instance (SQLMI). While GitHub Copilot could generate new database schemas and insert scripts, the availability of a specialized tool made the process more efficient. Using SSMA, the database was migrated to SQL Server with minimal effort. However, it is important to note that relationships in Microsoft Access may lack explicit names. In such cases, SSMA appends a GUID or uses one entirely to create unique foreign key names, which can result in confusing relationship names post-migration. Fortunately, GitHub Copilot can batch-rename these relationships in the generated SQL scripts, applying more meaningful naming conventions. By dropping and recreating the constraints, relationships become easier to understand and maintain. SSMA handles the bulk of the migration workload, allowing you to quickly obtain a fully functional SQL Server database containing all original data. In practice, renaming and recreating constraints often takes longer than the data migration itself. Prompt Used: # Context I want to refactor the #file:script.sql SQL script. Your task is to follow the below steps to analyse it and refactor it according to the specified rules. You are allowed to create / run any python scripts or terminal commands to assist in the analysis and refactoring process. # Analysis Phase Identify: Any warning comments Relations between tables Foreign key creation References to these foreign keys in 'MS_SSMA_SOURCE' metadata # Refactor Phase Refactor any SQL matching the following rules: - Create a new script file with the same name as the original but with a `.refactored.sql` extension - Rename any primary key constraints to follow the format PK_{table_name}_{column_name} - Rename any foreign key constraints like [TableName]${GUID} to FK_{child_table}_{parent_table} - Rename any indexes like [TableName]${GUID} to IDX_{table_name}_{column_name} - Ensure any updated foreign keys are updated elsewhere in the script - Identify which warnings flagged by the migration assistant need addressed # Summary Phase Create a summary file in markdown format with the following sections: - Summary of changes made - List of warnings addressed - List of foreign keys renamed - Any other relevant notes 🤖Bonus: Introduce Database Automation and Change Management As we now had a SQL database, we needed to consider how we would roll out changes to the database and we could introduce a formal tool to cater for this within the solution which was Liquibase. Prompt Used: # Context I want to refactor #file:db.changelog.xml. Your task is to follow the below steps to analyse it and refactor it according to the specified rules. You are allowed to create / run any python scripts or terminal commands to assist in the analysis and refactoring process. # Analysis Phase Analyse the generated changelog to identify the structure and content. Identify the tables, columns, data types, constraints, and relationships present in the database. Identify any default values, indexes, and foreign keys that need to be included in the changelog. Identify any vendor specific data types / fucntions that need to be converted to common Liquibase types. # Refactor Phase DO NOT modify the original #file:db.changelog.xml file in any way. Instead, create a new changelog file called `db.changelog-1-0.xml` to store the refactored changesets. The new file should follow the structure and conventions of Liquibase changelogs. You can fetch https://docs.liquibase.com/concepts/data-type-handling.html to get available Liquibase types and their mappings across RDBMS implementations. Copy the original changesets from the `db.changelog.xml` file into the new file Refactor the changesets according to the following rules: - The main changelog should only include child changelogs and not directly run migration operations - Child changelogs should follow the convention db.changelog-{version}.xml and start at 1-0 - Ensure data types are converted to common Liquibase data types. For example: - `nvarchar(max)` should be converted to `TEXT` - `datetime2` should be converted to `TIMESTAMP` - `bit` should be converted to `BOOLEAN` - Ensure any default values are retained but ensure that they are compatible with the liquibase data type for the column. - Use standard SQL functions like `CURRENT_TIMESTAMP` instead of vendor-specific functions. - Only use vendor specific data types or functions if they are necessary and cannot be converted to common Liquibase types. These must be documented in the changelog and summary. Ensure that the original changeset IDs are preserved for traceability. Ensure that the author of all changesets is "liquibase (generated)" # Validation Phase Validate the new changelog file against the original #file:db.changelog.xml to ensure that all changesets are correctly refactored and that the structure is maintained. Confirm no additional changesets are added that were not present in the original changelog. # Finalisation Phase Provide a summary of the changes made in the new changelog file. Document any vendor specific data types or functions that were used and why they could not be converted to common Liquibase types. Ensure the main changelog file (`db.changelog.xml`) is updated to include the new child changelog file (`db.changelog-1-0.xml`). 🤖Bonus: Synthetic Data Generation Since the legacy system lacked synthetic data for development or testing, GitHub Copilot was used to generate fake seed data. Care was taken to ensure all generated data was clearly fictional—using placeholders like ‘Fake Name’ and ‘Fake Town’—to avoid any confusion with real-world information. This step greatly improved the maintainability of the project, enabling developers to test features without handling sensitive or real data. 💫Second-Layer Migration: OpenAPI Specifications With data migration complete, the focus shifted to implementing an API-driven approach for data retrieval. Adopting modern standards, OpenAPI specifications were used to define new RESTful APIs for creating, reading, updating, and deleting data. Because these APIs mapped directly to underlying entities, GitHub Copilot efficiently generated the required endpoints and services in Node.js, utilizing a repository pattern. This approach not only provided robust APIs but also included comprehensive self-describing documentation, validation at the API boundary, automatic error handling, and safeguards against invalid data reaching business logic or database layers. 💫Third-Layer Migration: Business Logic The business logic, originally authored in VBA, was generally straightforward. GitHub Copilot translated this logic into its Node.js equivalent and created corresponding tests for each method. These tests were developed directly from the code, adding a layer of quality assurance that was absent in the original Access solution. The result was a set of domain services mirroring the functionality of their VBA predecessors, successfully completing the migration of the third layer. At this stage, the project had a new database, a fresh API tier, and updated business logic, all conforming to the latest organizational standards. The final major component was the user interface, an area where advances in GitHub Copilot’s capabilities became especially evident. 💫Fourth Layer: User Interface The modernization of the Access Forms user interface posed unique challenges. To minimize retraining requirements, the new system needed to retain as much of the original layout as possible, ensuring familiar placement of buttons, dropdowns, and other controls. At the same time, it was necessary to meet new accessibility standards and best practices. Some Access forms were complex, spanning multiple tabs and containing numerous controls. Manually describing each interface for redevelopment would have been time-consuming. Fortunately, newer versions of GitHub Copilot support image-based prompts, allowing screenshots of Access Forms to serve as context. Using these screenshots, Copilot generated Government Digital Service Views that closely mirrored the original application while incorporating required accessibility features, such as descriptive labels and field selectors. Although the automatically generated UI might not fully comply with all current accessibility standards, prompts referencing WCAG guidelines helped guide Copilot’s improvements. The generated interfaces provided a strong starting point for UX engineers to further refine accessibility and user experience to meet organizational requirements. 🤖Bonus: User Story Generation from the User Interface For organizations seeking a specification-driven development approach, GitHub Copilot can convert screenshots and business logic into user stories following the “As a … I want to … So that …” format. While not flawless, this capability is invaluable for systems lacking formal requirements, giving business analysts a foundation to build upon in future iterations. 🤖Bonus: Introducing MongoDB Towards the end of the modernization engagement, there was interest in demonstrating migration from SQL Server to MongoDB. GitHub Copilot can facilitate this migration, provided it is given adequate context. As with all NoSQL databases, the design should be based on application data access patterns—typically reading and writing related data together. Copilot’s ability to automate this process depends on a comprehensive understanding of the application’s data relationships and patterns. # Context The `<business_entity>` entity from the existing system needs to be added to the MongoDB schema. You have been provided with the following: - #file:documentation - System documentation to provide domain / business entity context - #file:db.changelog.xml - Liquibase changelog for SQL context - #file:mongo-erd.md - Contains the current Mongo schema Mermaid ERD. Create this if it does not exist. - #file:stories - Contains the user stories that will the system will be built around # Analysis Phase Analyse the available documentation and changelog to identify the structure, relationships, and business context of the `<business_entity>`. Identify: - All relevant data fields and attributes - Relationships with other entities - Any specific data types, constraints, or business rules Determine how this entity fits into the overall MongoDB schema: - Should it be a separate collection? - Should it be embedded in another document? - Should it be a reference to another collection for lookups or relationships? - Explore the benefit of denormalization for performance and business needs Consider the data access patterns and how this entity will be used in the application. # MongoDB Schema Design Using the analysis, suggest how the `<business_entity>` should be represented in MongoDB: - The name of the MongoDB collection that will represent this entity - List each field in the collection, its type, any constraints, and what it maps to in the original business context - For fields that are embedded, document the parent collection and how the fields are nested. Nested fields should follow the format `parentField->childField`. - For fields that are referenced, document the reference collection and how the lookup will be performed. - Provide any additional notes on indexing, performance considerations, or specific MongoDB features that should be used - Always use pascal case for collection names and camel case for field names # ERD Creation Create or update the Mermaid ERD in `mongo-erd.md` to include the results of your analysis. The ERD should reflect: - The new collection or embedded document structure - Any relationships with other collections/entities - The data types, constraints, and business rules that are relevant for MongoDB - Ensure the ERD is clear and follows best practices for MongoDB schema design Each entity in the ERD should have the following layout: **Entity Name**: The name of the MongoDB collection / schema **Fields**: A list of fields in the collection, including: - Field Name (in camel case) - Data Type (e.g., String, Number, Date, ObjectId) - Constraints (e.g. indexed, unique, not null, nullable) In this example, Liquibase was used as a changelog to supply the necessary context, detailing entities, columns, data types, and relationships. Based on this, Copilot could offer architectural recommendations for new document or collection types, including whether to embed documents or use separate collections with cache references for lookup data. Copilot can also generate an entity relationship diagram (ERD), allowing for review and validation before proceeding. From there, a new data access layer can be generated, configurable to switch between SQL Server and MongoDB as needed. While production environments typically standardize on a single database model, this demonstration showcased the speed and flexibility with which strategic architectural components can be introduced using GitHub Copilot. 👨💻Conclusion This modernization initiative demonstrated how strategic use of automation and best practices can transform legacy Microsoft Access solutions into scalable, maintainable architectures utilizing Node.js, SQL Server, MongoDB, and OpenAPI. By carefully planning each migration layer—from database and API specifications to business logic—the team preserved core functionality while introducing modern standards and enhanced capabilities. GitHub Copilot played a pivotal role, not only speeding up redevelopment but also improving code quality through automated documentation, test generation, and meaningful naming conventions. The result was a significant reduction in development time, with a robust, standards-compliant system delivered in just two weeks compared to an estimated six to eight months using traditional manual methods. This project serves as a blueprint for organizations seeking to modernize their Access-based applications, highlighting the efficiency gains and quality improvements that can be achieved by leveraging AI-powered tools and well-defined migration strategies. The approach ensures future scalability, easier maintenance, and alignment with contemporary enterprise requirements.Synthetic Monitoring in Application Insights Using Playwright: A Game-Changer
Monitoring the availability and performance of web applications is crucial to ensuring a seamless user experience. Azure Application Insights provides powerful synthetic monitoring capabilities to help detect issues proactively. However, Microsoft has deprecated two key features: (Deprecated) Multi-step web tests: Previously, these allowed developers to record and replay a sequence of web requests to test complex workflows. They were created in Visual Studio Enterprise and uploaded to the portal. (Deprecated) URL ping tests: These tests checked if an endpoint was responding and measured performance. They allowed setting custom success criteria, dependent request parsing, and retries. With these features being phased out, we are left without built-in logic to test application health beyond simple endpoint checks. The solution? Custom TrackAvailability tests using Playwright. What is Playwright? Playwright is a powerful end-to-end testing framework that enables automated browser testing for modern web applications. It supports multiple browsers (Chromium, Firefox, WebKit) and can run tests in headless mode, making it ideal for synthetic monitoring. Why Use Playwright for Synthetic Monitoring? Simulate real user interactions (login, navigate, click, etc.) Catch UI failures that simple URL ping tests cannot detect Execute complex workflows like authentication and transactions Integrate with Azure Functions for periodic execution Log availability metrics in Application Insights for better tracking and alerting Step-by-Step Implementation (Repo link) Set Up an Azure Function App Navigate to the Azure Portal. Create a new Function App. Select Runtime Stack: Node.js. Enable Application Insights. Install Dependencies In your local development environment, create a Node.js project: mkdir playwright-monitoring && cd playwright-monitoring npm init -y npm install /functions playwright applicationinsights dotenv Implement the Timer-Triggered Azure Function Create timerTrigger1.js: const { app } = require('@azure/functions'); const { runPlaywrightTests } = require('../playwrightTest.js'); // Import the Playwright test function app.timer('timerTrigger1', { schedule: '0 */5 * * * *', // Runs every 5 minutes handler: async (myTimer, context) => { try { context.log("Executing Playwright test..."); await runPlaywrightTests(context); context.log("Playwright test executed successfully!"); } catch (error) { context.log.error("Error executing Playwright test:", error); } finally { context.log("Timer function processed request."); } } }); Implement the Playwright Test Logic Create playwrightTest.js: require('dotenv').config(); const playwright = require('playwright'); const appInsights = require('applicationinsights'); // Debugging: Print env variable to check if it's loaded correctly console.log("App Insights Key:", process.env.APPLICATIONINSIGHTS_CONNECTION_STRING); // Initialize Application Insights appInsights .setup(process.env.APPLICATIONINSIGHTS_CONNECTION_STRING || process.env.APPINSIGHTS_INSTRUMENTATIONKEY) .setSendLiveMetrics(true) .setDistributedTracingMode(appInsights.DistributedTracingModes.AI_AND_W3C) .setAutoDependencyCorrelation(true) .setAutoCollectRequests(true) .setAutoCollectPerformance(true) .setAutoCollectExceptions(true) .setAutoCollectDependencies(true) .setAutoCollectConsole(true) .setUseDiskRetryCaching(true) // Enables retry caching for telemetry .setInternalLogging(true, true) // Enables internal logging for debugging .start(); const client = appInsights.defaultClient; async function runPlaywrightTests(context) { const timestamp = new Date().toISOString(); try { context.log(`[${timestamp}] Running Playwright login test...`); // Launch Browser const browser = await playwright.chromium.launch({ headless: true }); const page = await browser.newPage(); // Navigate to login page await page.goto('https://www.saucedemo.com/'); // Perform Login await page.fill('#user-name', 'standard_user'); await page.fill('#password', 'secret_sauce'); await page.click('#login-button'); // Verify successful login await page.waitForSelector('.inventory_list', { timeout: 5000 }); // Log Success to Application Insights client.trackAvailability({ name: "SauceDemo Login Test", success: true, duration: 5000, // Execution time runLocation: "Azure Function", message: "Login successful", time: new Date() }); context.log("✅ Playwright login test successful."); await browser.close(); } catch (error) { context.log.error("❌ Playwright login test failed:", error); // Log Failure to Application Insights client.trackAvailability({ name: "SauceDemo Login Test", success: false, duration: 0, runLocation: "Azure Function", message: error.message, time: new Date() }); } } module.exports = { runPlaywrightTests }; Configure Environment Variables Create a .env file and set your Application Insights connection string: APPLICATIONINSIGHTS_CONNECTION_STRING=<your_connection_string> Deploy and Monitor Deploy the Function App using Azure CLI: func azure functionapp publish <your-function-app-name> Monitor the availability results in Application Insights → Availability. Setting Up Alerts for Failed Tests To get notified when availability tests fail: Open Application Insights in the Azure portal. Go to Alerts → Create Alert Rule. Select Signal Type: Availability Results. Configure a condition where Success = 0 (Failure). Add an action group (email, Teams, etc.). Click Create Alert Rule. Conclusion With Playwright-based synthetic monitoring, you can go beyond basic URL ping tests and validate real user interactions in your application. Since Microsoft has deprecated Multi-step web tests and URL ping tests, this approach ensures better availability tracking, UI validation, and proactive issue detection in Application Insights.Building AI Agents: Workflow-First vs. Code-First vs. Hybrid
AI Agents are no longer just a developer’s playground. They’re becoming essential for enterprise automation, decision-making, and customer engagement. But how do you build them? Do you go workflow-first with drag-and-drop designers, code-first with SDKs, or adopt a hybrid approach that blends both worlds? In this article, I’ll walk you through the landscape of AI Agent design. We’ll look at workflow-first approaches with drag-and-drop designers, code-first approaches using SDKs, and hybrid models that combine both. The goal is to help you understand the options and choose the right path for your organization. Why AI Agents Need Orchestration Before diving into tools and approaches, let’s talk about why orchestration matters. AI Agents are not just single-purpose bots anymore. They often need to perform multi-step reasoning, interact with multiple systems, and adapt to dynamic workflows. Without orchestration, these agents can become siloed and fail to deliver real business value. Here’s what I’ve observed as the key drivers for orchestration: Complexity of Enterprise Workflows Modern business processes involve multiple applications, data sources, and decision points. AI Agents need a way to coordinate these steps seamlessly. Governance and Compliance Enterprises require control over how AI interacts with sensitive data and systems. Orchestration frameworks provide guardrails for security and compliance. Scalability and Maintainability A single agent might work fine for a proof of concept, but scaling to hundreds of workflows requires structured orchestration to avoid chaos. Integration with Existing Systems AI Agents rarely operate in isolation. They need to plug into ERP systems, CRMs, and custom apps. Orchestration ensures these integrations are reliable and repeatable. In short, orchestration is the backbone that turns AI Agents from clever prototypes into enterprise-ready solutions. Behind the Scenes I’ve always been a pro-code guy. I started my career on open-source coding in Unix and hardly touched the mouse. Then I discovered Visual Studio, and it completely changed my perspective. It showed me the power of a hybrid approach, the best of both worlds. That said, I won’t let my experience bias your ideas of what you’d like to build. This blog is about giving you the full picture so you can make the choice that works best for you. Workflow-First Approach Workflow-first platforms are more than visual designers and not just about drag-and-drop simplicity. They represent a design paradigm where orchestration logic is abstracted into declarative models rather than imperative code. These tools allow you to define agent behaviors, event triggers, and integration points visually, while the underlying engine handles state management, retries, and scaling. For architects, this means faster prototyping and governance baked into the platform. For developers, it offers extensibility through connectors and custom actions without sacrificing enterprise-grade reliability. Copilot Studio Building conversational agents becomes intuitive with a visual designer that maps prompts, actions, and connectors into structured flows. Copilot Studio makes this possible by integrating enterprise data and enabling agents to automate tasks and respond intelligently without deep coding. Building AI Agents using Copilot Studio Design conversation flows with adaptive prompts Integrate Microsoft Graph for contextual responses Add AI-driven actions using Copilot extensions Support multi-turn reasoning for complex queries Enable secure access to enterprise data sources Extend functionality through custom connectors Logic Apps Adaptive workflows and complex integrations are handled through a robust orchestration engine. Logic Apps introduces Agent Loop, allowing agents to reason iteratively, adapt workflows, and interact with multiple systems in real time. Building AI Agents using Logic Apps Implement Agent Loop for iterative reasoning Integrate Azure OpenAI for goal-driven decisions Access 1,400+ connectors for enterprise actions Support human-in-the-loop for critical approvals Enable multi-agent orchestration for complex tasks Provide observability and security for agent workflows Power Automate Multi-step workflows can be orchestrated across business applications using AI Builder models or external AI APIs. Power Automate enables agents to make decisions, process data, and trigger actions dynamically, all within a low-code environment. Building AI Agents using Power Automate Automate repetitive tasks with minimal effort Apply AI Builder for predictions and classification Call Azure OpenAI for natural language processing Integrate with hundreds of enterprise connectors Trigger workflows based on real-time events Combine flows with human approvals for compliance Azure AI Foundry Visual orchestration meets pro-code flexibility through Prompt Flow and Connected Agents, enabling multi-step reasoning flows while allowing developers to extend capabilities through SDKs. Azure AI Foundry is ideal for scenarios requiring both agility and deep customization. Building AI Agents using Azure AI Foundry Design reasoning flows visually with Prompt Flow Orchestrate multi-agent systems using Connected Agents Integrate with VS Code for advanced development Apply governance and deployment pipelines for production Use Azure OpenAI models for adaptive decision-making Monitor workflows with built-in observability tools Microsoft Agent Framework (Preview) I’ve been exploring Microsoft Agent Framework (MAF), an open-source foundation for building AI agents that can run anywhere. It integrates with Azure AI Foundry and Azure services, enabling multi-agent workflows, advanced memory services, and visual orchestration. With public preview live and GA coming soon, MAF is shaping how we deliver scalable, flexible agentic solutions. Enterprise-scale orchestration is achieved through graph-based workflows, human-in-the-loop approvals, and observability features. The Microsoft Agent Framework lays the foundation for multi-agent systems that are durable and compliant. Building AI Agents using Microsoft Agent Framework Coordinate multiple specialized agents in a graph Implement durable workflows with pause and resume Support human-in-the-loop for controlled autonomy Integrate with Azure AI Foundry for hosting and governance Enable observability through OpenTelemetry integration Provide SDK flexibility for custom orchestration patterns Visual-first platforms make building AI Agents feel less like coding marathons and more like creative design sessions. They’re perfect for those scenarios when you’d rather design than debug and still want the option to dive deeper when complexity calls. Pro-Code Approach Remember I told you how I started as a pro-code developer early in my career and later embraced a hybrid approach? I’ll try to stay neutral here as we explore the pro-code world. Pro-code frameworks offer integration with diverse ecosystems, multi-agent coordination, and fine-grained control over logic. While workflow-first and pro-code approaches both provide these capabilities, the difference lies in how they balance factors such as ease of development, ease of maintenance, time to deliver, monitoring capabilities, and other non-functional requirements. Choosing the right path often depends on which of these trade-offs matter most for your scenario. LangChain When I first explored LangChain, it felt like stepping into a developer’s playground for AI orchestration. I could stitch together prompts, tools, and APIs like building blocks, and I enjoyed the flexibility. It reminded me why pro-code approaches appeal to those who want full control over logic and integration with diverse ecosystems. Building AI Agents using LangChain Define custom chains for multi-step reasoning [it is called Lang“Chain”] Integrate external APIs and tools for dynamic actions Implement memory for context-aware conversations Support multi-agent collaboration through orchestration patterns Extend functionality with custom Python modules Deploy agents across cloud environments for scalability Semantic Kernel I’ve worked with Semantic Kernel when I needed more control over orchestration logic, and what stood out was its flexibility. It provides both .NET and Python SDKs, which makes it easy to combine natural language prompts with traditional programming logic. I found the planners and skills especially useful for breaking down goals into smaller steps, and connectors helped integrate external systems without reinventing the wheel. Building AI Agents using Semantic Kernel Create semantic functions for prompt-driven tasks Use planners for dynamic goal decomposition Integrate plugins for external system access Implement memory for persistent context across sessions Combine AI reasoning with deterministic code logic Enable observability and telemetry for enterprise monitoring Microsoft Agent Framework (Preview) Although I introduced MAF in the earlier section, its SDK-first design makes it relevant here as well for advanced orchestration and the pro-code nature… and so I’ll probably write this again in the Hybrid section. The Agent Framework is designed for developers who need full control over multi-agent orchestration. It provides a pro-code approach for defining agent behaviors, implementing advanced coordination patterns, and integrating enterprise-grade observability. Building AI Agents using Microsoft Agent Framework Define custom orchestration logic using SDK APIs Implement graph-based workflows for multi-agent coordination Extend agent capabilities with custom code modules Apply durable execution patterns with pause and resume Integrate OpenTelemetry for detailed monitoring and debugging Securely host and manage agents through Azure AI Foundry integration Hybrid Approach and decision framework I’ve always been a fan of both worlds, the flexibility of pro-code and the simplicity of workflow drag-and-drop style IDEs and GUIs. A hybrid approach is not about picking one over the other; it’s about balancing them. In practice, this to me means combining the speed and governance of workflow-first platforms with the extensibility and control of pro-code frameworks. Hybrid design shines when you need agility without sacrificing depth. For example, I can start with Copilot Studio to build a conversational agent using its visual designer. But if the scenario demands advanced logic or integration, I can call an Azure Function for custom processing, trigger a Logic Apps workflow for complex orchestration, or even invoke the Microsoft Agent Framework for multi-agent coordination. This flexibility delivers the best of both worlds, low-code for rapid development (remember RAD?) and pro-code for enterprise-grade customization with complex logic or integrations. Why go Hybrid Ø Balance speed and control: Rapid prototyping with workflow-first tools, deep customization with code. Ø Extend functionality: Call APIs, Azure Functions, or SDK-based frameworks from visual workflows. Ø Optimize for non-functional requirements: Address maintainability, monitoring, and scalability without compromising ease of development. Ø Enable interoperability: Combine connectors, plugins, and open standards for diverse ecosystems. Ø Support multi-agent orchestration: Integrate workflow-driven agents with pro-code agents for complex scenarios. The hybrid approach for building AI Agents is not just a technical choice but a design philosophy. When I need rapid prototyping or business automation, workflow-first is my choice. For multi-agent orchestration and deep customization, I go with code-first. Hybrid makes sense for regulated industries and large-scale deployments where flexibility and compliance are critical. The choice isn’t binary, it’s strategic. I’ve worked with both workflow-first tools like Copilot Studio, Power Automate, and Logic Apps, and pro-code frameworks such as LangChain, Semantic Kernel, and the Microsoft Agent Framework. Each approach has its strengths, and the decision often comes down to what matters most for your scenario. If rapid prototyping and business automation are priorities, workflow-first platforms make sense. When multi-agent orchestration, deep customization, and integration with diverse ecosystems are critical, pro-code frameworks give you the flexibility and control you need. Hybrid approaches bring both worlds together for regulated industries and large-scale deployments where governance, observability, and interoperability cannot be compromised. Understanding these trade-offs will help you create AI Agents that work so well, you’ll wonder if they’re secretly applying for your job! About the author Pradyumna (Prad) Harish is a Technology leader in the WW GSI Partner Organization at Microsoft. He has 26 years of experience in Product Engineering, Partner Development, Presales, and Delivery. Responsible for revenue growth through Cloud, AI, Cognitive Services, ML, Data & Analytics, Integration, DevOps, Open-Source Software, Enterprise Architecture, IoT, Digital strategies and other innovative areas for business generation and transformation; achieving revenue targets via extensive experience in managing global functions, global accounts, products, and solution architects across over 26 countries.Building an Enterprise RAG Pipeline in Azure with NVIDIA AI Blueprint for RAG and Azure NetApp Files
Transform your enterprise-grade RAG pipeline with NVIDIA AI and Azure NetApp Files. This post highlights the challenges of scaling RAG solutions and introduces NVIDIA's AI Blueprint adapted for Azure. Discover how Azure NetApp Files boosts performance and handles dynamic demands, enabling robust and efficient RAG workloads.
